For purposes herein, the term “cell” is used to describe a bounded geographic area in which a number of users are serviced by a set of transmit antennas often co-located and operating at a base-station. The antennas are used to jointly transmit signals to users, and the signals are produced by a single common physical layer mechanism. Given pathloss, the signal power received by a user from these antennas drops with increasing distance of a user from these antennas. Therefore, users receiving a suitable minimal signal level are often located in a bounded geographic area around such antennas. Neighboring cells (base-stations and antennas in neighboring cells) operate independently from each other in the respect that useful signals to a given user are only sent by antennas (the base-station) within a user's cell.
A classic example of such “cells” is shown in FIG. 1A. Referring to FIG. 1A, users are mapped to the base-station site that is “geographically closest”, resulting in the classic hexagonal pattern. For example, Cell 1 consists of a central set of 4 antennas at a single base-station, “BS1”, supporting a group of users including but not limited to “user1”, “user2”, “user3”, and “user4”. This “geographically closest” station rule makes sense in a model in which the received signal energy a user gets from any base-station (or antenna) decreases monotonically with distance from that station, and by the same mathematical function for every station. In general, with shadowing and other effects, the cell boundaries will not conform to such a regular structure, but such a situation has the same underlying properties of the regular structure. In the communication system of FIG. 1A, the transmitted signals themselves originating at each base-station can be transmitted using any technique from a variety of well-known techniques, such as, for example, single-input single-output (SISO) transmission; multiple input single output (MISO) transmission; multiple input multiple output (MIMO) transmission; and, multi-user MIMO (MU-MIMO) transmission whereby multiple antennas coordinate a joint concurrent transmission to multiple users. Underlying transmissions can be based on Orthogonal Frequency Division Multiplexing (OFDM), Code Division Multiple Access (CDMA), etc.
As mentioned, in such a scenario, it is well known that if neighboring base-stations use the same transmission resource, e.g. the same frequency band at the same time, that the users in a cell will experience interference from other cells. Such interference can be quite extreme near the edges of cells, thus limiting performance in such areas. This is a classic problem with any cellular structure, and is true for SISO, MIMO and MU-MIMO transmissions. Transmission resources may also include, in addition to slots in time, frequency and jointly in time and frequency, codes in CDMA, polarization of antennas, etc.
Classic cellular systems can control interference by using different frequencies in neighboring cells. For the hexagonal structure in FIG. 1A, one can use 3 different frequencies (with a frequency reuse factor of three) so that no two neighboring cells use the same frequency. This is illustrated in FIG. 1B. This allows for increased separation of cells (distance separation) that use the same frequency, and helps greatly in reducing the interference between cells (such interference is termed “inter-cell interference”). However, the efficiency of the system can be hurt because the frequency reuse approach by nature reduces the effective number of frequencies (the bandwidth) used for signaling information to users in each of the cells. In fact, with a frequency reuse factor of “F”, the useful signal energy to users can be reduced by a factor of “F”, potentially lowering throughputs to each user by up to the same factor despite the benefits of inter-cell interference reduction. Furthermore there are additional losses by not exploiting diversity among frequencies, and this can reduce the effective rates a user may receive even more. Note that his frequency reuse concept can be considered as some minimal, but fixed coordination between stations.
Coordinating transmissions across multiple cells can help to alleviate such inter-cell interference (ICI) effects, which are particularly harmful to users at the edge of cells (such users termed “edge users”). In the extreme case, jointly coordinating transmissions over every cell, using MU-MIMO as the underlying signaling, can have significant benefit in alleviating problems due to ICI. Indeed, under full coordination, where all base-stations coordinate with each other and transmissions from any and every station can serve any or every user, there is no concept of a cell. Here the MIMO downlink reduces to a single MIMO broadcast channel. This is also in essence a large Distributed Antenna System (DAS) in which all antennas at all locations are controlled by a central single entity, and in principle some resource from all antennas transmits to all users in its signaling range. Such a system, however, may not be practical in large realistic deployments with large numbers of base-stations, realistic pathloss effects, and large numbers of users. The complexity of coordinating all antennas, problems of asynchronony in reception of signals from highly geographically separated antennas to any given user, and the amount and latency of information that needs to be shared between remote base-stations (antennas) over the backbone infra-structure, can render such an ideal case impractical. Thus, full coordination strictly at the physical layer is hard to achieve over a large multi-cell system.
However, an encouraging result is that by using even limited (yet practical) levels of coordination, significant performance benefits can still be obtained over a conventional cellular architecture. In such a system, non-overlapping clusters of stations coordinate their transmissions. An example of this is shown in FIG. 2 where groups (clusters) of 3 adjacent cells are coordinated. For example, Cells 1, 2 and 3 coordinate transmissions, Cells 4, 5 and 6 coordinate transmissions, and so on. Each of these groups of three cells are now acting as a single “cell”, or what is termed herein as a “cluster”. Again, the concept of a “cell” is different from that in a classic “cellular” structure. However, because coordination is partial, even this system will inherently always have boundaries where users that see less favorable conditions. Specifically, such architectures do have cooperation boundaries at which ICI is significant and may severely limit the performance of “edge users”, where such users are now “cluster-edge” users. An example of such a user is “user(6)” in FIG. 2.
More specifically, in multi-cell, or even single-cell, systems, with a fixed cell structure or a fixed cluster structure, the rate and quality of service (QoS) a user receives depends strongly on its physical relationship with respect to the transmit antennas used to send it useful signals (send it information bearing signals). It also depends, importantly, on the user's relationship to antennas that are sending interfering signals, i.e. signals intended for other users. The net tradeoff a user experiences depends on both the useful signal term and the interference terms a user receives. The fixed structure fixes this tradeoff for a given user.
For example, one measure often used is in terms of the Signal to Interference and Noise Ratio (SINR) a user experiences. The SINR (or performance) tradeoffs can, for example, depend on a user's geographic location. Such tradeoffs can also on other effects such as shadowing, terrain, antenna heights, etc. Nonetheless, the nominal (average) SINR a user experiences at a given location in a fixed cluster or fixed cell structure is often given by (and fixed by) the structure.
If such clusters of antennas, both interfering and signaling, are fully coordinated in time, frequency and space, the signal powers can be improved, and the effect of interferences can be controlled and mitigated. For example, frequency reuse can be used in cluster structures as illustrated for cell structures in FIG. 1B. However, in all systems with a limited yet single coordination structure, there are limits to such improvements. This often means that users near the physical edge of a “coordination boundary” often will see impairments in performance, in particular due to interference from neighboring “cells” or clusters.
In addition, in a system with a limited yet single coordination structure, a user has no choice but to use the channels and antennas assigned to it. If a user is disadvantaged through geographic location or otherwise, nothing can be done to improve its situation beyond a certain limit.
Thus, all fixed-coordination pattern system inherently suffers from the problem of coordination boundaries.